Illicit Social Accounts? Anti-Money Laundering for Transactional Blockchains

In recent years, blockchain anonymity has led to more illicit accounts participating in various money laundering transactions. Existing studies typically detect money laundering transactions, known as AML (Anti-money Laundering), through learning transaction features on transaction graphs of transactional blockchains. However, transaction graphs fail to represent the accounts’ social features within transactional organizations. Account graphs reveal such features well, and detecting illicit accounts on account graphs provides a new perspective on AML. For example, it helps uncover illegal transactions whose transaction features are not distinct in transaction graphs, with a loose assumption that illicit accounts are likely involved in illegal transactions. In this paper, we propose a Social Attention Graph Neural Network ( $\textsf {SGNN}$ ) on account graphs converted from transaction graphs. To detect illicit accounts, $\textsf {SGNN}$ learns the social features on two sub-graphs, a heterogeneous graph and a hypergraph, extracted from the account graph, and fuses these features into account attribute vectors through attention. The experimental results on the Elliptic++ dataset demonstrate $\textsf {SGNN}$ ’s advances. It outperforms the best baseline by 14.18% in precision, 7.37% in F1 score, 0.96% in accuracy, and 0.64% in recall when detecting illicit accounts on account graphs, as well as detects 20.3% more recall of illegal transactions through these illicit accounts than state-of-the-art methods based on transaction graphs when the mappings between illegal transactions and illicit accounts are provided. Moreover, thanks to social features, $\textsf {SGNN}$ has a novel capability that works under many account scales and activity degrees. We release our code on https://github.com/CloudLab-NEU/SGNN.